Classification of positive and negative test cases using ensemble machine learning methods
Department of Information Technology, Faculty of Engineering, Natural and Medical Sciences, International Burch University, Bosnia and Herzegovina.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(01), 364–371.
Article DOI: 10.30574/wjaets.2024.12.1.0232
Publication history:
Received on 28 April 2024; revised on 10 June 2024; accepted on 12 June 2024
Abstract:
In today’s IT industry, fast software delivery is becoming a standard and the race to the market adds up to the pressure. To ensure the quality of the developed features, quality assurance engineers apply both manual and automated techniques, and adapt their approach to optimize the process of verification. Machine learning can help significantly with the automation of some traditionally manual processes. In this research, we are showing how the classification of test cases into positive and negative can be done using ensemble machine learning methods, and whether those have advantages and better results over the basic machine learning methods. The best result was obtained by the Gradient Boosting Classifier with accuracy of 94%. However, since there is just a 1% difference in accuracy when compared to regular Decision Tree, it would not be necessary to use ensemble methods for this specific problem.
Keywords:
Quality Assurance; Positive test cases; Negative test cases; Machine learning; Classification; Ensemble learning
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0